【摘要】 神经网络差分区分器具备良好的泛化能力和强大的学习能力，但目前仍缺乏完善且具有普适性的神经网络差分区分模型。为提升Simon32/64和Simeck32/64神经网络差分区分器的准确率和普适性，文章提出3个改进方向。首先，采用多密文对作为Simon32/64和Simeck32/64的输入，并将Inception网络模块引入神经网络模型，以改善过拟合现象。然后，将Simon32/64和Simeck32/64倒数第二轮的差分信息加入多密文对输入样本中，构造7～10轮和7～11轮神经网络差分区分器。最后，将多密文对与多面体差分结合，根据Simon32/64和Simeck32/64两种密码构造改进多面体差分区分器，提高已有多面体神经网络差分区分器的准确率。实验结果表明，8轮Simon32/64和Simeck32/64新型多面体神经网络差分区分器的准确率分别达到99.54%和99.67%。此外，利用10轮神经网络差分区分器对12轮Simon32/64和Simeck32/64开展最后一轮子密钥恢复攻击，在100次攻击实验中，攻击成功率分别达到86%和97%。更多还原

【Abstract】 Neural distinguishers have good generalisation ability as well as powerful learning ability,but there is still a lack of perfect and universal neural network distinguishing model.In order to increase the accuracy of the neural distinguishers of Simon32/64 and Simeck32/64,increase the generalizability of the neural differential distinguishers,this paper proposed three improvement directions.First,multiple ciphertext pairs were used as inputs to the Simon32/64 and Simeck32/64 neural distinguishers,and the Inception network module was added to the neural network model to improve the overfitting phenomenon.Then,added Simon32/64 and Simeck32/64 penultimate round difference information to the multi-ciphertext pair input samples,constructed the netural distinguishers of 7 to 10 rounds of Simon32/64 and 7 to 11 rounds of Simeck32/64.Finelly,multiple ciphertext pairs were combined with polyhedral difference,constructed polyhedral differential distinguishers for Simon32/64 and Simeck32/64.The accuracy of the polyhedral neural distinguishers were improved.The experimental results show that the new polyhedral netural distinguishers of 8-round of Simon32/64 reach the accuracy of 99.54% and 8-round of Simeck32/64 reach the accuracy of 99.67%.In addition,the improved netural distinguishers of the 10-round of Simon32/64 and Simeck32/64 are applied to the final round of key recovery attacks of 12-round of Simon32/64 and Simeck32/64,the success rate of the attacks respectively reaches 86% and 97% in 100 attack experiments.更多还原